Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning.

ABSTRACT: Understanding the complex interplay between human behavior, disease transmission and non-pharmaceutical interventions during the COVID-19 pandemic could provide valuable insights with which to focus future public health efforts. Cell phone mobility data offer a modern measurement instrument to investigate human mobility and behavior at an unprecedented scale. We investigate aggregated and anonymized mobility data, which measure how populations at the census-block-group geographic scale stayed at home in California, Georgia, Texas and Washington from the beginning of the pandemic. Using manifold learning techniques, we show that a low-dimensional embedding enables the identification of patterns of mobility behavior that align with stay-at-home orders, correlate with socioeconomic factors, c

SUBMITTER: Levin R 

PROVIDER: S-EPMC10766515 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature